IDEAS home Printed from https://ideas.repec.org/a/iab/iabjlr/v47i1-2p043-062.html
   My bibliography  Save this article

Estimation of standard errors and treatment effects in empirical economics : methods and applications

Author

Listed:
  • Hübler, Olaf

    (Institut für Empirische Wirtschaftsforschung, Universität Hannover)

Abstract

"This paper discusses methodological problems of standard errors and treatment effects. First, heteroskedasticity- and cluster-robust estimates are considered as well as problems with Bernoulli distributed regressors, outliers and partially identified parameters. Second, procedures to determine treatment effects are analyzed. Four principles are in the focus: difference-in-differences estimators, matching procedures, treatment effects in quantile regression analysis and regression discontinuity approaches. These methods are applied to Cobb-Douglas functions using IAB establishment panel data. Different heteroskedasticity-consistent procedures lead to similar results of standard errors. Cluster-robust estimates show evident deviates. Dummies with a mean near 0.5 have a smaller variance of the coefficient estimates than others. Not all outliers have a strong influence on significance. New methods to handle the problem of partially identified parameters lead to more efficient estimates. The four discussed treatment procedures are applied to the question whether company-level pacts affect the output. In contrast to unconditional difference-in-differences and to estimates without matching the company-level effect is positive but insignificant if conditional difference-in-differences, nearest-neighbor or Mahalanobis metric matching is applied. The latter result has to be specified under quantile treatment effects analysis. The higher the quantile the higher is the positive company-level pact effect and there is a tendency from insignificant to significant effects. A sharp regression discontinuity analysis shows a structural break at a probability of 0.5 that a company-level pact exists. No specific effect of the Great Recession can be detected. Fuzzy regression discontinuity estimates reveal that the company-level pact effect is significantly lower in East than in West Germany." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Hübler, Olaf, 2014. "Estimation of standard errors and treatment effects in empirical economics : methods and applications," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 47(1-2), pages 43-62.
    • Handle: RePEc:iab:iabjlr:v:47:i:1-2:p:043-062
    DOI: 10.1007/s12651-013-0135-0
    as

    Download full text from publisher

    File URL: https://doi.org/10.1007/s12651-013-0135-0
    Download Restriction: no
    File URL: https://libkey.io/10.1007/s12651-013-0135-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, January.
    2. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    3. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    4. Walter Krämer, 2011. "The Cult of Statistical Significance – What Economists Should and Should Not Do to Make their Data Talk," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 131(3), pages 455-468.
    5. Joseph P. Romano & Azeem M. Shaikh, 2010. "Inference for the Identified Set in Partially Identified Econometric Models," Econometrica, Econometric Society, vol. 78(1), pages 169-211, January.
    6. Francisco Cribari-Neto & Wilton Silva, 2011. "A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 129-146, June.
    7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    8. Jorg Stoye, 2009. "More on Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 77(4), pages 1299-1315, July.
    9. Puhani, Patrick A., 2012. "The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models," Economics Letters, Elsevier, vol. 115(1), pages 85-87.
    10. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    11. Jeffrey M. Wooldridge, 2003. "Cluster-Sample Methods in Applied Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 133-138, May.
    12. Guido W. Imbens & Charles F. Manski, 2004. "Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 72(6), pages 1845-1857, November.
    13. Daniel S. Hamermesh, 2000. "The Craft of Labormetrics," ILR Review, Cornell University, ILR School, vol. 53(3), pages 363-380, April.
    14. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 107, University of California, Davis, Department of Economics.
    15. Victor Chernozhukov & Han Hong & Elie Tamer, 2007. "Estimation and Confidence Regions for Parameter Sets in Econometric Models," Econometrica, Econometric Society, vol. 75(5), pages 1243-1284, September.
    16. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    17. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    18. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    19. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    20. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hübler, Olaf, 2013. "Methods in empirical economics - a selective review with applications," Hannover Economic Papers (HEP) dp-513, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Rok Spruk & Mitja Kovac, 2018. "Inefficient Growth," Review of Economics and Institutions, Università di Perugia, vol. 9(2).
    4. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 106, University of California, Davis, Department of Economics.
    5. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    6. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    7. Mitja Kovac & Salvini Datta & Rok Spruk, 2021. "Pharmaceutical Product Liability, Litigation Regimes, and the Propensity to Patent: An Empirical Firm-Level Investigation," SAGE Open, , vol. 11(2), pages 21582440211, April.
    8. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    9. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    10. Armstrong, Timothy B., 2015. "Asymptotically exact inference in conditional moment inequality models," Journal of Econometrics, Elsevier, vol. 186(1), pages 51-65.
    11. Sung Jae Jun & Yoonseok Lee & Youngki Shin, 2016. "Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 302-311, April.
    12. Fluvià, Modest & Rigall-I-Torrent, Ricard & Espinet, Josep Maria & Garriga, Anna & Saló, Albert, 2011. "Precios implícitos de los atributos de los productos turísticos: ¿Qué esconde el efecto de la localización?/Implicit Prices of the Attributes of Tourism Products: What is Hidden Behind Location?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 781-802, Diciembre.
    13. Klishchuk Bogdan & Zelenyuk Valentin, 2012. "Impact of Services LIberalization on Firm Level Productivity in Eastern Europe and Central Asia," EERC Working Paper Series 12/03e, EERC Research Network, Russia and CIS.
    14. Yuichi Kitamura & Jörg Stoye, 2018. "Nonparametric Analysis of Random Utility Models," Econometrica, Econometric Society, vol. 86(6), pages 1883-1909, November.
    15. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    16. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    17. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    18. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2018. "Monte Carlo Confidence Sets for Identified Sets," Econometrica, Econometric Society, vol. 86(6), pages 1965-2018, November.
    19. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers 22/14, Institute for Fiscal Studies.
    20. Donald W. K. Andrews & Xiaoxia Shi, 2013. "Inference Based on Conditional Moment Inequalities," Econometrica, Econometric Society, vol. 81(2), pages 609-666, March.

    More about this item

    Keywords

    Bundesrepublik Deutschland ; Ostdeutschland ; Westdeutschland ; Auswirkungen ; Bündnis für Arbeit ; empirische Forschung ; Fehler ; Forschungsmethode ; IAB-Betriebspanel ; Kausalanalyse ; Regressionsanalyse ; Rezession ; Schätzung ; Arbeitsmarktforschung ; Wirtschaftsforschung ; 2006-2010;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J53 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Labor-Management Relations; Industrial Jurisprudence
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iab:iabjlr:v:47:i:1-2:p:043-062. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: IAB, Geschäftsbereich Wissenschaftliche Fachinformation und Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/iabbbde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.